Adversarial Representation Learning for Robust Patient-Independent Epileptic Seizure Detection

Zhang, Xiang; Yao, Lina; Dong, Manqing; Liu, Zhe; Zhang, Yu; Li, Yong

doi:10.1109/jbhi.2020.2971610

Cited by 90 publications

(60 citation statements)

References 25 publications

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“…In general, the frequency range of EEG signals extends from 0 to 100 Hz, which is divided into sub-bands: delta (<4 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma (>30 Hz). Raw EEG signals may contain noise from different sources, such as electric or electromagnetic fields.…”

Section: F Effect Of the Frequency Bandmentioning

confidence: 99%

“…The extracted feature vector was used to train a Random Forest classifier to achieve classification accuracy of 95%. In addition to all of the above, Zhang et al [14] proposed a patient-independent diagnostic approach for epileptic seizure. The proposed approach refines the seizure-specific representation by eliminating the intersubject noise through adversarial training.…”

Section: Introductionmentioning

confidence: 99%

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Common Spatial Pattern Technique With EEG Signals for Diagnosis of Autism and Epilepsy Disorders

et al. 2021

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Electroencephalogram (EEG) signals reflect the activities or electrical disturbances in neurons in the human brain. Therefore, these signals are vital for diagnosing certain brain disorders. This study mainly focused on the diagnosis of epilepsy and autism spectrum disorders (ASDs) through the analysis and processing of EEGs. In this study, artifacts were removed from the EEG datasets using Independent Component Analysis and were filtered using a fifth-order band-pass Butterworth filter to remove interference and noise. Next, new methods were used to extract the features of EEGs using common spatial pattern (CSP). It is known that conventional CSP uses variance. However, here the use of entropy, energy, and band power with CSP was proposed to extract features of EEGs. Then, in our investigation, four techniques were employed for classification, namely, linear discriminant analysis, support vector machine, k-nearest neighbor (KNN), and artificial neural network, with the aim of comparing the proposed methods and recommending the optimal combination for the diagnosis of epilepsy and ASDs. Finally, the effects of segment length, frequency band, and reduction number on the results were investigated. Two EEG datasets were employed to verify the proposed methods: the King Abdulaziz University dataset (for ASD) and the MIT dataset (for epilepsy). The results indicated that the extracted features based on CSP and band LBP produced the best performance and that the combination of CSP-LBP-KNN provided the best performance with average classification accuracy of approximately 98.46% and 98.62% for diagnosing ASDs and epilepsy, respectively.

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Section: F Effect Of the Frequency Bandmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Common Spatial Pattern Technique With EEG Signals for Diagnosis of Autism and Epilepsy Disorders

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Experiments were carried out on the EEG dataset from Bonn University and came to the result that the average accuracy is 97.17% along with average sensitivity of 93.11% [21]. Zhang et al proposed an explainable epileptic seizure detection model to the pure seizure-specific representation for EEG signal through adversarial training, in order to overcome the discrepancy of different subjects [22].…”

Section: Introductionmentioning

confidence: 99%

Cross-Subject Seizure Detection in EEGs Using Deep Transfer Learning

Zhang

Wang

Xiao

et al. 2020

Computational and Mathematical Methods in Medicine

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Electroencephalography (EEG) plays an import role in monitoring the brain activities of patients with epilepsy and has been extensively used to diagnose epilepsy. Clinically reading tens or even hundreds of hours of EEG recordings is very time consuming. Therefore, automatic detection of seizure is of great importance. But the huge diversity of EEG signals belonging to different patients makes the task of seizure detection much challenging, for both human experts and automation methods. We propose three deep transfer convolutional neural networks (CNN) for automatic cross-subject seizure detection, based on VGG16, VGG19, and ResNet50, respectively. The original dataset is the CHB-MIT scalp EEG dataset. We use short time Fourier transform to generate time-frequency spectrum images as the input dataset, while positive samples are augmented due to the infrequent nature of seizure. The model parameters pretrained on ImageNet are transferred to our models. And the fine-tuned top layers, with an output layer of two neurons for binary classification (seizure or nonseizure), are trained from scratch. Then, the input dataset are randomly shuffled and divided into three partitions for training, validating, and testing the deep transfer CNNs, respectively. The average accuracies achieved by the deep transfer CNNs based on VGG16, VGG19, and ResNet50 are 97.75%, 98.26%, and 96.17% correspondingly. On those results of experiments, our method could prove to be an effective method for cross-subject seizure detection.

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“…There have been many researchers using different data to help doctors make more accurate diagnosis. For example, electroencephalogram (EEG) data are used to provide more detailed diagnosis of epilepsy [6], and multimodal data (PET, MRI, single nucleotide polymorphism, etc.) are used to determine Alzheimer's disease [7].…”

Section: Introductionmentioning

confidence: 99%

Classification and Biomarker Exploration of Autism Spectrum Disorders Based on Recurrent Attention Model

Yang

2020

IEEE Access

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Adversarial Representation Learning for Robust Patient-Independent Epileptic Seizure Detection

Cited by 90 publications

References 25 publications

Common Spatial Pattern Technique With EEG Signals for Diagnosis of Autism and Epilepsy Disorders

Common Spatial Pattern Technique With EEG Signals for Diagnosis of Autism and Epilepsy Disorders

Cross-Subject Seizure Detection in EEGs Using Deep Transfer Learning

Classification and Biomarker Exploration of Autism Spectrum Disorders Based on Recurrent Attention Model

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